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    This study introduces visual analytics to help understand complex rule-based machine learning models. It aids in identifying illogical rules to improve model trustworthiness and interpretability.

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    Area of Science:

    • Machine Learning
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Rule-based machine learning models are often considered interpretable.
    • Model complexity and rule interdependencies hinder human understanding.
    • Extracted rule sets can contain logical inconsistencies despite high accuracy.

    Purpose of the Study:

    • To introduce a visual analytics methodology for exploring rule-based model logic.
    • To support systematic analysis of rule sets and their alignment with domain knowledge.
    • To enable detection and refinement of illogical or implausible rules.

    Main Methods:

    • Integration of overview visualizations, interactive filtering, contradiction analysis, and topic modeling.
    • Methodology supports reasoning with and without labeled data.
    • Demonstrated through case studies on vessel movement classification and COVID-19 prediction.

    Main Results:

    • Visual analytics facilitates the detection of illogical or counterintuitive rules.
    • The approach aids in assessing the impact of identified inconsistencies.
    • Case studies show improved model critique beyond performance metrics.

    Conclusions:

    • Visual analytics enhances the interpretability and trustworthiness of rule-based models.
    • The methodology enables domain-relevant insights through logic-focused critique.
    • Supports systematic refinement of complex machine learning models.